Overview

Dataset statistics

Number of variables14
Number of observations166
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 KiB
Average record size in memory112.8 B

Variable types

Categorical1
Numeric13

Alerts

nm_pol has a high cardinality: 157 distinct valuesHigh cardinality
rape is highly overall correlated with assualt murdersHigh correlation
robbery is highly overall correlated with theft and 1 other fieldsHigh correlation
theft is highly overall correlated with robbery and 1 other fieldsHigh correlation
assualt murders is highly overall correlated with rapeHigh correlation
totarea is highly overall correlated with crime/area and 1 other fieldsHigh correlation
totalcrime is highly overall correlated with robbery and 1 other fieldsHigh correlation
crime/area is highly overall correlated with totarea and 1 other fieldsHigh correlation
area is highly overall correlated with totarea and 1 other fieldsHigh correlation
nm_pol is uniformly distributedUniform
murder has 20 (12.0%) zerosZeros
rape has 2 (1.2%) zerosZeros
gangrape has 80 (48.2%) zerosZeros
sexual harassement has 2 (1.2%) zerosZeros

Reproduction

Analysis started2023-04-08 07:21:59.856898
Analysis finished2023-04-08 07:22:18.218305
Duration18.36 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

nm_pol
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct157
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
RAJOURI GARDEN
 
2
BAWANA
 
2
ANANDVIHAR
 
2
KASHMERE GATE
 
2
BINDAPUR
 
2
Other values (152)
156 

Length

Max length19
Median length15
Mean length11.150602
Min length5

Characters and Unicode

Total characters1851
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)89.2%

Sample

1st rowCHITRANJAN PARK
2nd rowDABRI
3rd rowMALVIYA NAGAR
4th rowCHANDNI MAHAL
5th rowMODEL TOWN

Common Values

ValueCountFrequency (%)
RAJOURI GARDEN 2
 
1.2%
BAWANA 2
 
1.2%
ANANDVIHAR 2
 
1.2%
KASHMERE GATE 2
 
1.2%
BINDAPUR 2
 
1.2%
SARITA VIHAR 2
 
1.2%
SAFDARJUNG ENCLAVE 2
 
1.2%
KALKAJI 2
 
1.2%
PRASHANT VIHAR 2
 
1.2%
CHITRANJAN PARK 1
 
0.6%
Other values (147) 147
88.6%

Length

2023-04-08T12:52:18.293308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nagar 27
 
9.3%
vihar 13
 
4.5%
puri 6
 
2.1%
colony 6
 
2.1%
pur 4
 
1.4%
bagh 4
 
1.4%
enclave 4
 
1.4%
south 4
 
1.4%
new 3
 
1.0%
gate 3
 
1.0%
Other values (185) 216
74.5%

Most occurring characters

ValueCountFrequency (%)
A 365
19.7%
R 183
 
9.9%
N 128
 
6.9%
124
 
6.7%
I 119
 
6.4%
H 98
 
5.3%
U 70
 
3.8%
G 66
 
3.6%
E 65
 
3.5%
L 64
 
3.5%
Other values (20) 569
30.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1715
92.7%
Space Separator 124
 
6.7%
Other Punctuation 9
 
0.5%
Decimal Number 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 365
21.3%
R 183
 
10.7%
N 128
 
7.5%
I 119
 
6.9%
H 98
 
5.7%
U 70
 
4.1%
G 66
 
3.8%
E 65
 
3.8%
L 64
 
3.7%
S 61
 
3.6%
Other values (15) 496
28.9%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
124
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1715
92.7%
Common 136
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 365
21.3%
R 183
 
10.7%
N 128
 
7.5%
I 119
 
6.9%
H 98
 
5.7%
U 70
 
4.1%
G 66
 
3.8%
E 65
 
3.8%
L 64
 
3.7%
S 61
 
3.6%
Other values (15) 496
28.9%
Common
ValueCountFrequency (%)
124
91.2%
. 9
 
6.6%
- 1
 
0.7%
2 1
 
0.7%
3 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 365
19.7%
R 183
 
9.9%
N 128
 
6.9%
124
 
6.7%
I 119
 
6.4%
H 98
 
5.3%
U 70
 
3.8%
G 66
 
3.6%
E 65
 
3.5%
L 64
 
3.5%
Other values (20) 569
30.7%

murder
Real number (ℝ)

Distinct11
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4819277
Minimum0
Maximum10
Zeros20
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:18.404304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35.75
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.8192475
Coefficient of variation (CV)0.80968007
Kurtosis-0.49447128
Mean3.4819277
Median Absolute Deviation (MAD)2
Skewness0.71654435
Sum578
Variance7.9481563
MonotonicityNot monotonic
2023-04-08T12:52:18.501270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 30
18.1%
2 29
17.5%
0 20
12.0%
4 20
12.0%
3 17
10.2%
6 12
 
7.2%
7 11
 
6.6%
5 8
 
4.8%
8 7
 
4.2%
10 6
 
3.6%
ValueCountFrequency (%)
0 20
12.0%
1 30
18.1%
2 29
17.5%
3 17
10.2%
4 20
12.0%
5 8
 
4.8%
6 12
 
7.2%
7 11
 
6.6%
8 7
 
4.2%
9 6
 
3.6%
ValueCountFrequency (%)
10 6
 
3.6%
9 6
 
3.6%
8 7
 
4.2%
7 11
 
6.6%
6 12
 
7.2%
5 8
 
4.8%
4 20
12.0%
3 17
10.2%
2 29
17.5%
1 30
18.1%

rape
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.76506
Minimum0
Maximum43
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:18.615114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median10.5
Q316
95-th percentile26.75
Maximum43
Range43
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7365082
Coefficient of variation (CV)0.65758339
Kurtosis0.84131267
Mean11.76506
Median Absolute Deviation (MAD)4.5
Skewness0.8985198
Sum1953
Variance59.85356
MonotonicityNot monotonic
2023-04-08T12:52:18.727149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
9 12
 
7.2%
7 11
 
6.6%
2 10
 
6.0%
8 10
 
6.0%
14 10
 
6.0%
5 10
 
6.0%
15 9
 
5.4%
6 8
 
4.8%
11 8
 
4.8%
4 7
 
4.2%
Other values (23) 71
42.8%
ValueCountFrequency (%)
0 2
 
1.2%
1 4
 
2.4%
2 10
6.0%
3 5
3.0%
4 7
4.2%
5 10
6.0%
6 8
4.8%
7 11
6.6%
8 10
6.0%
9 12
7.2%
ValueCountFrequency (%)
43 1
 
0.6%
31 1
 
0.6%
30 1
 
0.6%
29 2
1.2%
28 2
1.2%
27 2
1.2%
26 1
 
0.6%
25 2
1.2%
24 4
2.4%
23 4
2.4%

gangrape
Real number (ℝ)

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93373494
Minimum0
Maximum6
Zeros80
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:18.825114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2167296
Coefficient of variation (CV)1.3030781
Kurtosis2.773624
Mean0.93373494
Median Absolute Deviation (MAD)1
Skewness1.6197481
Sum155
Variance1.4804308
MonotonicityNot monotonic
2023-04-08T12:52:18.913114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 80
48.2%
1 47
28.3%
2 21
 
12.7%
3 11
 
6.6%
4 3
 
1.8%
5 3
 
1.8%
6 1
 
0.6%
ValueCountFrequency (%)
0 80
48.2%
1 47
28.3%
2 21
 
12.7%
3 11
 
6.6%
4 3
 
1.8%
5 3
 
1.8%
6 1
 
0.6%
ValueCountFrequency (%)
6 1
 
0.6%
5 3
 
1.8%
4 3
 
1.8%
3 11
 
6.6%
2 21
 
12.7%
1 47
28.3%
0 80
48.2%

robbery
Real number (ℝ)

Distinct70
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.259036
Minimum1
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:19.064116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.25
Q116.25
median28
Q346
95-th percentile83.5
Maximum126
Range125
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation24.730286
Coefficient of variation (CV)0.72186169
Kurtosis1.5954103
Mean34.259036
Median Absolute Deviation (MAD)14
Skewness1.2366286
Sum5687
Variance611.58704
MonotonicityNot monotonic
2023-04-08T12:52:19.213114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 12
 
7.2%
28 6
 
3.6%
15 6
 
3.6%
35 5
 
3.0%
31 5
 
3.0%
41 4
 
2.4%
13 4
 
2.4%
25 4
 
2.4%
1 4
 
2.4%
12 4
 
2.4%
Other values (60) 112
67.5%
ValueCountFrequency (%)
1 4
2.4%
2 1
 
0.6%
3 1
 
0.6%
5 3
1.8%
6 1
 
0.6%
8 2
1.2%
9 4
2.4%
10 3
1.8%
11 4
2.4%
12 4
2.4%
ValueCountFrequency (%)
126 1
0.6%
125 1
0.6%
99 2
1.2%
96 1
0.6%
93 2
1.2%
88 1
0.6%
84 1
0.6%
82 1
0.6%
81 1
0.6%
79 1
0.6%

theft
Real number (ℝ)

Distinct142
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400.01205
Minimum41
Maximum1273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:19.355116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile107
Q1244.25
median371
Q3505.75
95-th percentile812.5
Maximum1273
Range1232
Interquartile range (IQR)261.5

Descriptive statistics

Standard deviation223.5406
Coefficient of variation (CV)0.55883467
Kurtosis1.8078041
Mean400.01205
Median Absolute Deviation (MAD)129
Skewness1.0702033
Sum66402
Variance49970.4
MonotonicityNot monotonic
2023-04-08T12:52:19.598182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457 4
 
2.4%
437 3
 
1.8%
256 2
 
1.2%
180 2
 
1.2%
469 2
 
1.2%
336 2
 
1.2%
425 2
 
1.2%
383 2
 
1.2%
320 2
 
1.2%
466 2
 
1.2%
Other values (132) 143
86.1%
ValueCountFrequency (%)
41 1
0.6%
65 1
0.6%
66 1
0.6%
74 1
0.6%
83 2
1.2%
86 1
0.6%
104 1
0.6%
107 2
1.2%
116 1
0.6%
120 1
0.6%
ValueCountFrequency (%)
1273 1
0.6%
1251 1
0.6%
1042 1
0.6%
945 1
0.6%
895 1
0.6%
838 1
0.6%
832 1
0.6%
817 1
0.6%
814 1
0.6%
808 1
0.6%

assualt murders
Real number (ℝ)

Distinct49
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.957831
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:19.737186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median17
Q324
95-th percentile46.5
Maximum68
Range67
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.101632
Coefficient of variation (CV)0.69109339
Kurtosis1.598345
Mean18.957831
Median Absolute Deviation (MAD)8
Skewness1.2228709
Sum3147
Variance171.65276
MonotonicityNot monotonic
2023-04-08T12:52:19.873186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8 10
 
6.0%
23 9
 
5.4%
9 8
 
4.8%
19 7
 
4.2%
5 7
 
4.2%
13 7
 
4.2%
14 7
 
4.2%
17 7
 
4.2%
24 6
 
3.6%
16 6
 
3.6%
Other values (39) 92
55.4%
ValueCountFrequency (%)
1 3
 
1.8%
2 1
 
0.6%
3 4
 
2.4%
4 4
 
2.4%
5 7
4.2%
6 6
3.6%
7 4
 
2.4%
8 10
6.0%
9 8
4.8%
10 6
3.6%
ValueCountFrequency (%)
68 1
 
0.6%
63 1
 
0.6%
60 1
 
0.6%
54 1
 
0.6%
52 1
 
0.6%
48 1
 
0.6%
47 3
1.8%
45 1
 
0.6%
42 1
 
0.6%
41 1
 
0.6%

sexual harassement
Real number (ℝ)

Distinct31
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4698795
Minimum0
Maximum40
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:20.015186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.25
Q14
median8
Q312.75
95-th percentile23
Maximum40
Range40
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation7.2251033
Coefficient of variation (CV)0.7629562
Kurtosis3.6628481
Mean9.4698795
Median Absolute Deviation (MAD)4
Skewness1.6732075
Sum1572
Variance52.202118
MonotonicityNot monotonic
2023-04-08T12:52:20.133186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 17
 
10.2%
5 15
 
9.0%
8 13
 
7.8%
10 12
 
7.2%
7 11
 
6.6%
3 10
 
6.0%
9 8
 
4.8%
2 8
 
4.8%
6 8
 
4.8%
12 8
 
4.8%
Other values (21) 56
33.7%
ValueCountFrequency (%)
0 2
 
1.2%
1 7
4.2%
2 8
4.8%
3 10
6.0%
4 17
10.2%
5 15
9.0%
6 8
4.8%
7 11
6.6%
8 13
7.8%
9 8
4.8%
ValueCountFrequency (%)
40 1
 
0.6%
38 1
 
0.6%
34 1
 
0.6%
33 1
 
0.6%
30 1
 
0.6%
28 1
 
0.6%
26 1
 
0.6%
24 1
 
0.6%
23 2
1.2%
21 3
1.8%

totarea
Real number (ℝ)

Distinct134
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7453896.6
Minimum878452.78
Maximum70469487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:20.272185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum878452.78
5-th percentile1277296.8
Q11955090.3
median2975208.7
Q36082122.8
95-th percentile35794244
Maximum70469487
Range69591034
Interquartile range (IQR)4127032.5

Descriptive statistics

Standard deviation11602075
Coefficient of variation (CV)1.5565114
Kurtosis8.6317904
Mean7453896.6
Median Absolute Deviation (MAD)1331566.8
Skewness2.8954522
Sum1.2373468 × 109
Variance1.3460814 × 1014
MonotonicityNot monotonic
2023-04-08T12:52:20.406186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30068547.98 3
 
1.8%
6149745.104 3
 
1.8%
2975208.719 3
 
1.8%
14105371.6 3
 
1.8%
2231365.619 2
 
1.2%
1576237.38 2
 
1.2%
2463668.046 2
 
1.2%
3095888.323 2
 
1.2%
3748441.999 2
 
1.2%
4104570.166 2
 
1.2%
Other values (124) 142
85.5%
ValueCountFrequency (%)
878452.7789 1
0.6%
885072.498 1
0.6%
1021961.219 2
1.2%
1076492.634 1
0.6%
1205110.776 1
0.6%
1247661.874 1
0.6%
1255396.299 1
0.6%
1275858.528 1
0.6%
1281611.56 1
0.6%
1283551.172 1
0.6%
ValueCountFrequency (%)
70469487.22 1
 
0.6%
51545474.92 1
 
0.6%
49822649.53 1
 
0.6%
48582358.64 1
 
0.6%
42836353.41 1
 
0.6%
40327922.92 2
1.2%
38911866.78 1
 
0.6%
35895594.95 1
 
0.6%
35490190.9 2
1.2%
30068547.98 3
1.8%

totalcrime
Real number (ℝ)

Distinct143
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean478.87952
Minimum62
Maximum1433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:20.549186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile131.75
Q1295.25
median461
Q3616
95-th percentile941.25
Maximum1433
Range1371
Interquartile range (IQR)320.75

Descriptive statistics

Standard deviation251.00855
Coefficient of variation (CV)0.52415805
Kurtosis1.6936038
Mean478.87952
Median Absolute Deviation (MAD)158
Skewness0.98322534
Sum79494
Variance63005.294
MonotonicityNot monotonic
2023-04-08T12:52:20.681186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
509 3
 
1.8%
245 3
 
1.8%
512 2
 
1.2%
705 2
 
1.2%
303 2
 
1.2%
491 2
 
1.2%
571 2
 
1.2%
496 2
 
1.2%
526 2
 
1.2%
417 2
 
1.2%
Other values (133) 144
86.7%
ValueCountFrequency (%)
62 1
0.6%
71 1
0.6%
79 1
0.6%
90 1
0.6%
106 2
1.2%
112 1
0.6%
114 1
0.6%
131 1
0.6%
134 1
0.6%
139 1
0.6%
ValueCountFrequency (%)
1433 1
0.6%
1421 1
0.6%
1180 1
0.6%
1179 1
0.6%
1011 1
0.6%
983 1
0.6%
973 1
0.6%
955 1
0.6%
949 1
0.6%
918 1
0.6%

long
Real number (ℝ)

Distinct165
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.186804
Minimum76.91555
Maximum77.33267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:20.826535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum76.91555
5-th percentile77.037627
Q177.121842
median77.20295
Q377.256358
95-th percentile77.306507
Maximum77.33267
Range0.41712
Interquartile range (IQR)0.134515

Descriptive statistics

Standard deviation0.087788084
Coefficient of variation (CV)0.0011373458
Kurtosis-0.21783211
Mean77.186804
Median Absolute Deviation (MAD)0.0641
Skewness-0.56025868
Sum12813.009
Variance0.0077067477
MonotonicityNot monotonic
2023-04-08T12:52:20.959495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.30612 2
 
1.2%
77.2492 1
 
0.6%
77.06744 1
 
0.6%
77.1196 1
 
0.6%
77.24299 1
 
0.6%
77.30367 1
 
0.6%
77.07894 1
 
0.6%
77.26733 1
 
0.6%
77.05849 1
 
0.6%
77.15868 1
 
0.6%
Other values (155) 155
93.4%
ValueCountFrequency (%)
76.91555 1
0.6%
76.96085 1
0.6%
76.96681 1
0.6%
76.98269 1
0.6%
76.99466 1
0.6%
77.00361 1
0.6%
77.01564 1
0.6%
77.0228 1
0.6%
77.03599 1
0.6%
77.04254 1
0.6%
ValueCountFrequency (%)
77.33267 1
0.6%
77.31849 1
0.6%
77.31708 1
0.6%
77.31653 1
0.6%
77.316 1
0.6%
77.31306 1
0.6%
77.31178 1
0.6%
77.30817 1
0.6%
77.30662 1
0.6%
77.30617 1
0.6%

lat
Real number (ℝ)

Distinct165
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.641116
Minimum28.46549
Maximum28.85321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:21.094496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum28.46549
5-th percentile28.52901
Q128.598382
median28.647675
Q328.684777
95-th percentile28.733395
Maximum28.85321
Range0.38772
Interquartile range (IQR)0.086395

Descriptive statistics

Standard deviation0.066989468
Coefficient of variation (CV)0.0023389266
Kurtosis0.17305321
Mean28.641116
Median Absolute Deviation (MAD)0.043755
Skewness-0.075556197
Sum4754.4253
Variance0.0044875889
MonotonicityNot monotonic
2023-04-08T12:52:21.230496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.62543 2
 
1.2%
28.53632 1
 
0.6%
28.62046 1
 
0.6%
28.65151 1
 
0.6%
28.50957 1
 
0.6%
28.60733 1
 
0.6%
28.74766 1
 
0.6%
28.65955 1
 
0.6%
28.56411 1
 
0.6%
28.64751 1
 
0.6%
Other values (155) 155
93.4%
ValueCountFrequency (%)
28.46549 1
0.6%
28.49385 1
0.6%
28.49893 1
0.6%
28.50165 1
0.6%
28.50957 1
0.6%
28.52149 1
0.6%
28.52299 1
0.6%
28.52678 1
0.6%
28.52882 1
0.6%
28.52958 1
0.6%
ValueCountFrequency (%)
28.85321 1
0.6%
28.80659 1
0.6%
28.79903 1
0.6%
28.79654 1
0.6%
28.75841 1
0.6%
28.74766 1
0.6%
28.74286 1
0.6%
28.73664 1
0.6%
28.73358 1
0.6%
28.73284 1
0.6%

crime/area
Real number (ℝ)

Distinct163
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.5703
Minimum4.3954886
Maximum792.02551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:21.381496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.3954886
5-th percentile10.399571
Q154.204542
median135.77629
Q3230.13107
95-th percentile412.58989
Maximum792.02551
Range787.63003
Interquartile range (IQR)175.92652

Descriptive statistics

Standard deviation143.51411
Coefficient of variation (CV)0.86158283
Kurtosis3.5036038
Mean166.5703
Median Absolute Deviation (MAD)85.584345
Skewness1.5816031
Sum27650.67
Variance20596.3
MonotonicityNot monotonic
2023-04-08T12:52:21.518497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135.776292 2
 
1.2%
244.4852641 2
 
1.2%
13.04307194 2
 
1.2%
192.5297308 1
 
0.6%
49.0313434 1
 
0.6%
69.22702931 1
 
0.6%
116.0225301 1
 
0.6%
7.065059834 1
 
0.6%
293.7489292 1
 
0.6%
15.79723771 1
 
0.6%
Other values (153) 153
92.2%
ValueCountFrequency (%)
4.395488595 1
0.6%
4.722542641 1
0.6%
5.033543391 1
0.6%
5.557572904 1
0.6%
6.839984655 1
0.6%
7.065059834 1
0.6%
8.419749759 1
0.6%
9.116036206 1
0.6%
10.17182469 1
0.6%
11.08281089 1
0.6%
ValueCountFrequency (%)
792.0255138 1
0.6%
724.0979268 1
0.6%
678.4415415 1
0.6%
606.5861022 1
0.6%
551.349914 1
0.6%
494.1975293 1
0.6%
429.6329345 1
0.6%
429.5008123 1
0.6%
423.8300765 1
0.6%
378.869332 1
0.6%

area
Real number (ℝ)

Distinct134
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4538966
Minimum0.87845278
Maximum70.469487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-08T12:52:21.664497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.87845278
5-th percentile1.2772968
Q11.9550903
median2.9752087
Q36.0821228
95-th percentile35.794244
Maximum70.469487
Range69.591034
Interquartile range (IQR)4.1270325

Descriptive statistics

Standard deviation11.602075
Coefficient of variation (CV)1.5565114
Kurtosis8.6317904
Mean7.4538966
Median Absolute Deviation (MAD)1.3315668
Skewness2.8954522
Sum1237.3468
Variance134.60814
MonotonicityNot monotonic
2023-04-08T12:52:21.796496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.06854798 3
 
1.8%
6.149745104 3
 
1.8%
2.975208719 3
 
1.8%
14.1053716 3
 
1.8%
2.231365619 2
 
1.2%
1.57623738 2
 
1.2%
2.463668046 2
 
1.2%
3.095888323 2
 
1.2%
3.748441999 2
 
1.2%
4.104570166 2
 
1.2%
Other values (124) 142
85.5%
ValueCountFrequency (%)
0.878452779 1
0.6%
0.885072498 1
0.6%
1.021961219 2
1.2%
1.076492634 1
0.6%
1.205110776 1
0.6%
1.247661874 1
0.6%
1.255396299 1
0.6%
1.275858528 1
0.6%
1.28161156 1
0.6%
1.283551172 1
0.6%
ValueCountFrequency (%)
70.46948722 1
 
0.6%
51.54547492 1
 
0.6%
49.82264953 1
 
0.6%
48.58235864 1
 
0.6%
42.83635341 1
 
0.6%
40.32792292 2
1.2%
38.91186678 1
 
0.6%
35.89559495 1
 
0.6%
35.4901909 2
1.2%
30.06854798 3
1.8%

Interactions

2023-04-08T12:52:16.490811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.250944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.537669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.859675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.148917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.438657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.871847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.217730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.572785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.894997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.330082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.613425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.010843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.586810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.345669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.625668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.953676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.243908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.533696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.967847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.316729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.668785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.094026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.418586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.715054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.109811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.677810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.432670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.710670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.040678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.329875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.622659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.060849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.409729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.758785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.187997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.509716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.810811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.203810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.776811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.527669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.802668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.137676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.429610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.720659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.161862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.512729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.856785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.288000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.608717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.916811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.308810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.877810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.624669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.896670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.232675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.523576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.817189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.266861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.615730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.954785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.388997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.707715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.020811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.410810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.976812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.719669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.988669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.332676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.622577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.914842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.372862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.718730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.054786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.485001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.800714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.126809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.626846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.081811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.823670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.085669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.432560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.722576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.016841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.478858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.824730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.160785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.589997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.903714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.236810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.731809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.189809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:00.930670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.268677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.539560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.828576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.123842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.587859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.933729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.269000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.698031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.009715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.351810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.846810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.295832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.029673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.368677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.638982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.929577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.225842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.693859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.041730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.373999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.805080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.114715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.462812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:15.955810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.401832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.131670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.465676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.740789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.031657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.325842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.799731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.147785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.477997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:11.909080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.214995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.575811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.063810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.496833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.222670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.553676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.830788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.122660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.418848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:07.893731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.246786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.572997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.003079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.305030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.675810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.160810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.606833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.331670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.659677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:03.939788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.231657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.528847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.005729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.358786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.683997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.114079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.410024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.789812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.275810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:17.716833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:01.434671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:02.762676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:04.046790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:05.336658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:06.647851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:08.114733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:09.467785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:10.793043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:12.222079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:13.514426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:14.901810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-08T12:52:16.384810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-04-08T12:52:22.018496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
murderrapegangraperobberytheftassualt murderssexual harassementtotareatotalcrimelonglatcrime/areaarea
murder1.0000.4820.2290.3380.1590.3290.323-0.1050.2290.0430.0400.220-0.105
rape0.4821.0000.3290.2960.2930.6510.363-0.0820.366-0.0130.1630.286-0.082
gangrape0.2290.3291.0000.1560.1790.2760.223-0.0250.201-0.0740.0210.136-0.025
robbery0.3380.2960.1561.0000.6670.3080.3960.1020.722-0.2690.1330.3370.102
theft0.1590.2930.1790.6671.0000.4160.3100.1080.990-0.1460.0870.4670.108
assualt murders0.3290.6510.2760.3080.4161.0000.441-0.0440.472-0.0590.2240.344-0.044
sexual harassement0.3230.3630.2230.3960.3100.4411.000-0.0530.372-0.0260.1980.269-0.053
totarea-0.105-0.082-0.0250.1020.108-0.044-0.0531.0000.113-0.220-0.060-0.7871.000
totalcrime0.2290.3660.2010.7220.9900.4720.3720.1131.000-0.1570.1110.4650.113
long0.043-0.013-0.074-0.269-0.146-0.059-0.026-0.220-0.1571.000-0.1690.114-0.220
lat0.0400.1630.0210.1330.0870.2240.198-0.0600.111-0.1691.0000.146-0.060
crime/area0.2200.2860.1360.3370.4670.3440.269-0.7870.4650.1140.1461.000-0.787
area-0.105-0.082-0.0250.1020.108-0.044-0.0531.0000.113-0.220-0.060-0.7871.000

Missing values

2023-04-08T12:52:17.876114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-08T12:52:18.108107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nm_polmurderrapegangraperobberytheftassualt murderssexual harassementtotareatotalcrimelonglatcrime/areaarea
0CHITRANJAN PARK261354421972659329.53751277.2492028.53632192.5297312.659330
1DABRI82807924026163401013.42839777.0860028.61268116.7299123.401013
2MALVIYA NAGAR32813369463151379853.57283777.2041828.52989606.5861021.379854
3CHANDNI MAHAL181235291975570696.13258877.2360828.64361105.5523385.570696
4MODEL TOWN041453939142689157.08546677.1936928.70257173.2885012.689157
5ANANDVIHAR101409945724154558969.69261977.2937328.65335135.7762924.558970
6KASHMERE GATE0931734017121627909.97439877.2261828.66645244.4852641.627910
7GOVIND PURI14331692558992712.06074177.2653228.5306782.4000598.992712
8BINDAPUR441644171182568079.50050977.0661828.60910198.2025872.568080
9NEW FRIENDS COLONY6714760914104045386.71069477.2676028.56234171.5534384.045387
nm_polmurderrapegangraperobberytheftassualt murderssexual harassementtotareatotalcrimelonglatcrime/areaarea
156MUNDKA150112391134.858236e+0727076.9608528.670425.55757348.582359
157BHAJANPURI31942519319281.015799e+0729177.2644028.7012828.64739510.157992
158ZAFRABAD120366431.576237e+067977.2729728.6790950.1193541.576237
159WELCOME02015337551.805524e+0636477.2747928.67341201.6034781.805524
160NANDNAGARI92623058034112.028157e+0669277.3081728.69853341.1964712.028157
161G.T.B. ENCLAVE1101386672.874304e+0611477.3160028.6849639.6617702.874304
162NEW USMANPUR08110580953.093393e+0661377.2620728.67202198.1642563.093393
163SONIA VIHAR6251253004284.453887e+0640777.2561128.7112891.3808464.453887
164KARAWAL NAGAR7152263341972.937547e+0641077.2745028.73284139.5722552.937547
165GOKULPURI262223061981.566344e+0636577.2808528.70225233.0267481.566344